Research as part of the course on Industrial Economics and Foreign Trades
Electronic items are integral to daily life and their popularity has led to a surge of customer feedback through online shopping platforms like Amazon. Sentiment analysis of electronic reviews on E-Commerce platforms is crucial for identifying customer preferences and areas for product improvement. This study employs natural language processing techniques, machine learning and deep learning models to analyze electronic reviews and classify their sentiment as positive, neutral, or negative (coded as 1, 0, or -1, respectively). The aim of this study is to provide insights into customer satisfaction with electronic items and identify potential areas for improvement. Additionally, we evaluated the performance of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms in sentiment analysis and compared their accuracy rates.